{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d774ecce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64) (1797,)\n",
      "[[ 0.  0.  5. ...  0.  0.  0.]\n",
      " [ 0.  0.  0. ... 10.  0.  0.]\n",
      " [ 0.  0.  0. ... 16.  9.  0.]\n",
      " ...\n",
      " [ 0.  0.  1. ...  6.  0.  0.]\n",
      " [ 0.  0.  2. ... 12.  0.  0.]\n",
      " [ 0.  0. 10. ... 12.  1.  0.]] [0 1 2 ... 8 9 8]\n",
      "1797\n",
      "[[ 0.         -0.33501649 -0.04308102 ... -1.14664746 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  0.54856067 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -1.09493684 ...  1.56568555  1.6951369\n",
      "  -0.19600752]\n",
      " ...\n",
      " [ 0.         -0.33501649 -0.88456568 ... -0.12952258 -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649 -0.67419451 ...  0.8876023  -0.5056698\n",
      "  -0.19600752]\n",
      " [ 0.         -0.33501649  1.00877481 ...  0.8876023  -0.26113572\n",
      "  -0.19600752]]\n",
      "[[ 1.91421366 -0.95450157 -3.94603482 ... -0.01797902  0.04795038\n",
      "   0.01912424]\n",
      " [ 0.58898033  0.9246358   3.92475494 ... -1.14415907  0.03774402\n",
      "   0.37167996]\n",
      " [ 1.30203906 -0.31718883  3.02333293 ...  0.48730406 -1.35695937\n",
      "  -0.10701564]\n",
      " ...\n",
      " [ 1.02259599 -0.14791087  2.46997365 ... -0.15253558  0.0509132\n",
      "   0.59550456]\n",
      " [ 1.07605522 -0.38090625 -2.45548693 ... -0.44673737 -0.46840846\n",
      "   0.16065193]\n",
      " [-1.25770233 -2.22759088  0.28362789 ...  0.43650024 -0.92270619\n",
      "  -0.06156133]]\n",
      "(1797, 34)\n",
      "直接比对预测值与真实值：\n",
      " [[ True  True  True ...  True  True  True]\n",
      " [ True  True  True ...  True  True  True]\n",
      " [ True  True  True ...  True  True  True]\n",
      " ...\n",
      " [ True  True  True ...  True  True  True]\n",
      " [ True  True  True ...  True  True  True]\n",
      " [ True  True  True ...  True  True  True]]\n",
      "准确率为：\n",
      " 0.92\n",
      "this is a test file!\n"
     ]
    },
    {
     "data": {
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FB0uZ7rAqpYwanscKn6o8Dw+e6vv7EHCqYV2HaVjXYVR8p7lUB1GprIgqdhfLyITubEgQhbogBAOARlSbsMvI7A+7MrKU58prkKoCJy+S3VQlK0yK1R4eIsWrYAp+DiMqmKoGTFVDJ8KklDDyT1vwuD3yuDz7HwPfB9f2Cz6GtrkyleHe/xhchHxp8dKYVSA1+dmgn6nIFinTlam+A/tGVVM19/VFgHTSq10vQq96as5BVEPsn8ixJGmZa5kkqTCvsN4/QzRfhGAA0AB81hc37Kr0VYamTHmtV16fNzR9McOVoSxXljxuT6P9UZnsRbIbU3VhUvjUtGWly1Tpq9SOH3dEhkmJhk21mQ4XDJjiVTARJqWEkVGWOysUCFUNk6JCphhhUqzQKcOVIY/bExU6Zbr2twWPF75/zDHEeEx2VdOy4mUa/cLoWvc7r+95qlzln4bSqaBTUscEAHVBEJWaIApId4RgAFAPsUKYeGFXhbdCLuMKhV3ZGdmhP6pTZVnxsjotkv3qglfVIb9DzClqtQmTqpsOl0iYVOsFuefXbndEcxlXRHVRvPAmVpgUtS28gsnliQ6RqoRJscKnUMDkit/WEGFSuurVrpcGdxpcq+t+SOch6tWulxatWtSAIwOQjgiiCKKAdEMIBgAJClZ3hQc54ZVewYXsK32V8lmfPG6P3MYtj9ujHFeOMlwZTW7K0MwfZ9ap3y2f3JLkkTQ/VcOkuFVCNVQNhU+DC7ZXDZGqC59iVjJVbQt+T5jkCDcNuUkXvnFhQhWgLuPSjYNvbIRRAc5QNfCJ1eb1eeO2VdevpraI7QkegyCKIApobgjBAKAKa21ENVfVuzOGh12Vvkq5Xe79YZcnJ1Tp1RSVVZZp2dZlmrdpnhZsWqBPvv8k1UOqN7dxxwx4woMf716v3C63Wha0TGgaXNUwKW51U6CCKbg9VgVTxDpNhEloAoZ1Hab7j7+/xrUAXcalB054oMlMf0b9JCNkqRqMROxXTVu8/RpiXDW1NfTxg0FJ6HlYYOL1eSVJ2/Zui2qrrl9V4ftW1y9ivyrHC7YRRAFobprmX2kA0EjCA61YVV7hi9YHq7uCYUaOJ0cel6fJ/odYubdcy7cu17yN8/yh1+YFWrJliSp8FfU+dreW3dS1VdfEKphqmjIXY79EK5kSDZMWfe2fxtX3qL71fu2AE1zQ/wJ1btlZD85+MOZC+UM6D9GNg29s8ACsNkFKQ7TV5RhV25MRGkWNpZHDmboGKdUeP4EApqa2qtXTEf1c1YylDq+tMdqK3cWStP/Odo04lnjvc31DLQBIN4RgAJoFa23cyq5Q2OX1r90VrO4KVnTlenKbfOVOhbdCy7Yu04JNC0JVXouLF9d+zawEPXP6M9xZCkixquvtxJquVN1aPIe3P1zPnP6Mlm9drtlrZ2tX+S7lefJ0TOdjdEibQyTtr1iR/He3laSte7aGjhc+loix1SL8SUZYkoxwpjZBQ3g4E9UvyeFMst6ThgxSUt1W19fWUMeMZ6lrqSSpfYv2Ce0PAEg+QjAAjhOs7ooXdoVPdZS0/66MGVmh75vyv25W+iq1fOvyUOA1f9N8Ld6yWGXesoT6d2vZTQM6DNCAwgEa0H6A7v/yfn2z/puEzx9cJBuAX33DqHgLRNd07OoqNYLbYrUZ+dfiCbb3PaCv+h7QN6qfFPnHvdv4/yGgIKsgqq26frVpq9reXMOZhmgDAACEYADSmM/6IgKuqndn9Pr8d2SstP7gK1jN5XF5lOfJU4Yro0lXd0n+wOu7bd+FqrvmbZqnxVsWa1/lvoT6d23ZVQPaBwKvDgPUv7C/WmW3itjn1/o1i2TDEWpzl7Lq2msKo6puq27dGykQQsWZbhQKo8z+wCIZ6+zU1C+R8CwouH2Ve5UkqWN+x4jtof3qGHQBAAA0FkIwAGmhNmGXpNB6UVkZWcpz5TX56i7JX8G2cvtKf3XXxvmav3m+Fm5emHDg1aWgi/q376/D2x+uAe0HqF9hP7XJaVNjPxbJRrJVFzzF2xarPZHQKnxbomFUrAAoGEaFT2WLFy7Fa6t6zNoEVtW1VRdYNabgOD1uT6OfGwAAIBkIwQA0KT7rixt2VfoqVekNBF/WK6/PG5q+mOHKUJYrSx63J2oh3abI6/Pq++3fa/6m+aFF6xduXqg9FXsS6t8pv5O/wivsK5HAK56mskg2kiuRKqfahFFV13mKF1ZVF+CEvo8TAFUNo+pSzdQQgVNNARsAAACaPkIwAClhrZXXeqPuyBgr7KrwVshlXKGwKzsjO3SXwHTgsz59v/37iEXrF2xeoN0VuxPqf2CLA0NBV7DKq21u26SPc1jXYRrWdZiWFS/TzB9nqrS8VPmZ+Tqu63GsAVYPiVY5xdov2L+2YVSwvbqQJ9QeJwCqbRhVXbhU1361bQMAAACqkx5/QQJIa8HqrvCwK7zSK7iQfaWvUj7rk8ftkdu45XF7lOPKUYYrIy2quyT/a129Y7Xmb5of+lqweYF2le9KqH+HvA4Ri9YPaD9AB+Qd0MCjjtSrXS9nhl42fmBUNXTy717zdL6GDKOCIU+sMCqZwVFtAqtE2wAAAICmiBAMQNJYayOquarenTE87Kr0Vcrtcu8Puzw5oUqvdGGt1Q87f4hYtH7h5oUqKStJqH9hXmHEovUDCgc0i9umJ1rlVFPwVJuFzCu8FZKkzbs31ymMCoawscKoZAVHtWlLNOgCAAAAsF/6/LUJoEkJD7RiVXmFL1ofrO7KcGWEAi+Py5NWf6Rba7WmZE1E4LVg0wLtLNuZUP92ue0ipjMOaD9AHVp0aOBR112wei98ofxkhFHh60VJ1d+Zri5hVLxwqNhdLEnq0KJDg0/JixewAQAAAEgtQjAA1bLWxq3sCoVdXv/aXcHqrmBFV64nVx6XR26XO9Uvo1astVpbsnb/lMbN/scd+3Yk1L9NTpuIsGtA+wE6sMWBTTYQCa/gK/eWq8JbISvrn4aqQOgUI4wKBUBxwqi6LlreENVTK1wrJEkH5h/YSO8qAAAAgKaGEAxASLC6K17YFT7VUdL+uzJmZIW+b6pBTzzWWq0vXb//Lo2BKq/t+7Yn1L91duuoRes75nds0u+Dz/pCYVfw5x36Wbqz1CKzhTwujzLdmdUGVXUNswAAAAAgFQjBgGYoWN0VfkfG8O+9Pv8dGSutP/gKVnN5XB7lefKU4cpIu+ouyf+6N+zaELFo/fxN87V179aE+rfKahW1aH3ngs5NPtgJhl1Vq7wyXZnK8+QpMztTHrc/9AqGXx63J9XDBgAAAICkIgQDHC68mqumsEvyV3d5XB5lZWQpz5WXltVdQRt3bQwFXfM2zdP8TfNVvKc4ob4FWQVRi9Z3bdm1yb8Xsaq8ggFm1SqvYPCV6c5Mm7tvAgAAAEBdEYIBDhFcyDxW2FXpq1SlNxB8Wa+8Pm9o+luGK0NZrix53J60DkI27drkX7tr4/41vDbv3pxQ3/zMfPVv31+Htz889HhQy4OafOAlRVZ5Vfoq5bO+iCovT3ZkhRdVXgAAAACaK0IwIM1Ya+W13ph3ZKzwVajSG1i/y/orwFzGFQq7sjOyQ3dpTGdbdm+JXLR+43xt3L0xob4tMluof2H/iEXru7XqlhYBIFVeAAAAAFB36f2XMOBwweoun/XJWqstu7dEVHoFF7IPVgB53B65jVset0fZrmz/3f3SPADZumdrxHTG+Zvma8OuDQn1zfPkqV9hv4jAq3vr7mnznoSHXRW+Cqq8AAAAAKAeCMGAJsBaGzF1serdGYOLmW/ctVGVvspQdZfH7VGOJydU6ZXutu3dFrVo/brSdQn1zcnIUf/2/dW/sH/oLo3dW3dPmwX8g1Velb7KULVXsMor052pvMw8eVyeiAovqrwAAAAAIHHp/1czkGbCq7diTWkMX7Q+WN1lZWWMUX5WvjwuT1qsVVWT7Xu3a8HmBaEqrwWbFmhNyZqE+mZnZPsrvMIWrT+kzSFpE3hJ0VVeXp/XH3CFVXmFV3hlujPT+iYFAAAAAJBqhGBAA7HWxqzsirhbo9e/UH2lr1JulztU0ZXryZXH5QmFOhtc/ul/me7MVL6kOtuxb4c/8ApbtP7HnT8m1Dfbna3DCg8LBV6Htz9ch7Q5JK0q36jyAgAAAIDUS5+/IoEmLFjdVXUaY9XqrkpfpSTtvytjRlboe6dU+JSUlWjBpgURi9av3rk6ob5Z7iwddsBhEWt4Hdrm0LRb5yr48y/3lkdVeeV6cpWZnUmVFwAAAAA0MkIwoBaC1V2hSq4q33t9/rs2Vlp/8BWs5vK4PMrz5CnDlZFWU/ZqUlpWqoWbF4amM87bNE+rdqxKqG+mO1N92vWJCLx6te2VdoFX8OYFFb6KqCovj8vjD72q3K2RKi8AAAAAaHyEYEAcEdMWawi7JH91l8flUVZGlvJceY6r7NlVvksLNy+MWLR+5faVCfX1uDzq3a63BrQfEFq0vle7Xmk5vZMqLwAAAABIT4RgaPaClTxVw67QFEZvIPiyXnl93tD0xQxXhrJcWfK4PY6r6tldvluLtiwKLVo/f9N8rdy2Ula2xr4Zrgx/4BW2aH3vdr2VlZHVCCNPrqpVXsE7c1ZX5RW+lhsAAAAAoOkgBEOzYa2V13pj3pExGHZV+ipVaf2VPi7jCoVd2RnZ8rg9abUYe6L2VuzVwi0LIxat/27bd/JZX4193catXu16RSxa37tdb2VnZDfCyJMvXpVXROAVVuUVbKPKCwAAAACaPuf9RQ8osoIn1rTG4EL2lb5K+axPHrdHbuOWx+1RtitbGa4Mx1V3Sf7Aa/GWxRGL1i/ftjyhwMtlXOrVtlfEGl592vVRjienEUaefPGqvDJMhjLdmVFVXsHwiyovAAAAAEhPhGBIa9baiDsvVg2+wsOuUMjhypDH7VGOJydU6eVE+yr3acmWJaHpjPM3zdfyrcvltd4a+7qMS4e2OTQi8Op7QN+0Dbyk2lV5hQdfVHkBAAAAgDM4869/OFJ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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52  0  0  0  0  0  0  0  0  0]\n",
      " [ 4 38  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 49  0  1  0  0  0  1  0]\n",
      " [ 1  0  2 36  0  0  0  0  1  0]\n",
      " [ 1  0  0  0 50  0  0  0  0  0]\n",
      " [ 1  0  0  3  0 47  0  0  0  0]\n",
      " [ 1  1  1  0  0  0 42  0  0  0]\n",
      " [ 1  0  0  1  1  0  0 41  0  0]\n",
      " [ 3  0  1  0  0  0  0  1 34  0]\n",
      " [ 3  0  0  0  0  2  0  0  1 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      1.00      0.87        52\n",
      "           1       0.97      0.90      0.94        42\n",
      "           2       0.92      0.96      0.94        51\n",
      "           3       0.90      0.90      0.90        40\n",
      "           4       0.96      0.98      0.97        51\n",
      "           5       0.96      0.92      0.94        51\n",
      "           6       1.00      0.93      0.97        45\n",
      "           7       0.98      0.93      0.95        44\n",
      "           8       0.92      0.87      0.89        39\n",
      "           9       1.00      0.83      0.91        35\n",
      "\n",
      "    accuracy                           0.93       450\n",
      "   macro avg       0.94      0.92      0.93       450\n",
      "weighted avg       0.94      0.93      0.93       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from common.utils import plot_learning_curve\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "\n",
    "def show(image):\n",
    "    test = image.reshape((8, 8))  # 从一维变为二维，这样才能被显示\n",
    "    print(test.shape)  # 查看是否是二维数组\n",
    "    # print(test)\n",
    "    plt.imshow(test, cmap=plt.cm.gray)  # 显示灰色图像\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def standard_demo(data):\n",
    "    transfer = StandardScaler()\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def pca_demo(data):\n",
    "    transfer = PCA(n_components=0.92)\n",
    "    data_new = transfer.fit_transform(data)\n",
    "    print(data_new)\n",
    "    return data_new\n",
    "\n",
    "\n",
    "def code_demo(data, label):\n",
    "    numFiles = len(data)\n",
    "    hwLabels = np.zeros([numFiles, 10])  # 用于存放对应的one-hot标签\n",
    "    for i in range(numFiles):  # 遍历所有的样本\n",
    "        digit = label[i]\n",
    "        hwLabels[i][digit] = 1.0  # 将对应的one-hot标签置1\n",
    "    return hwLabels\n",
    "\n",
    "\n",
    "def MLP_demo(data, label):\n",
    "    # 对标签进行独热编码\n",
    "    label = code_demo(data, label)\n",
    "    # print(label)\n",
    "    # # 独热编码逆转回数字标签   argmax返回列表最大值索引\n",
    "    # label = label.argmax(axis=1)\n",
    "    # print(label)\n",
    "    # 划分数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, label, random_state=6)\n",
    "    # 训练模型\n",
    "    estimate = MLPClassifier(hidden_layer_sizes=(100,),\n",
    "                             activation='relu', solver='lbfgs',\n",
    "                             learning_rate_init=0.001, max_iter=2000)\n",
    "    estimate.fit(X_train, y_train)  # 模型构建好了\n",
    "    # 模型评估的两种方法：1：直接比对预测值与真实值；\n",
    "    y_predict = estimate.predict(X_test)\n",
    "    print(\"直接比对预测值与真实值：\\n\", y_test == y_predict)\n",
    "    # 2：计算准确率\n",
    "    score = estimate.score(X_test, y_test)\n",
    "    print(\"准确率为：\\n\", score)\n",
    "    # 绘制学习曲线!!!!\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)  # 10折交叉验证\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6), dpi=144)\n",
    "    plot_learning_curve(ax, estimate, \"Learn Curve\",\n",
    "                        X_train, y_train, ylim=(0.0, 1.01), cv=cv)\n",
    "    plt.show()\n",
    "    # 混淆矩阵\n",
    "    y_test = y_test.argmax(axis=1)\n",
    "    y_predict = y_predict.argmax(axis=1)\n",
    "    cm = confusion_matrix(y_test, y_predict)\n",
    "    print(cm)\n",
    "    # 可视化显示混淆矩阵\n",
    "    # annot = True 显示数字 ，fmt参数不使用科学计数法进行显示\n",
    "    ax = sn.heatmap(cm, annot=True, fmt='.20g')\n",
    "    ax.set_title('confusion matrix')  # 标题\n",
    "    ax.set_xlabel('predict')  # x轴\n",
    "    ax.set_ylabel('true')  # y轴\n",
    "    plt.show()\n",
    "    # 计算精确率与召回率\n",
    "    report = classification_report(y_test, y_predict, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "                                   target_names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
    "    print(report)\n",
    "\n",
    "\n",
    "# 按间距中的绿色按钮以运行脚本。\n",
    "if __name__ == '__main__':\n",
    "    # 查看数据集，以图片显示\n",
    "    print(X.shape, y.shape)\n",
    "    print(X, y)\n",
    "    print(len(X))\n",
    "    # show(X[1795])\n",
    "    # 数据集预处理（标准化、特征降维）\n",
    "    X_new = standard_demo(X)\n",
    "    X_new = pca_demo(X_new)\n",
    "    print(X_new.shape)  # 从64维降到了40维\n",
    "    # 机器学习建模\n",
    "    # 调参\n",
    "    # 模型评估\n",
    "    # 学习曲线\n",
    "    MLP_demo(X_new, y)  # 数据已经准备好了\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c3d6d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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